Lec8
Spatial Localization and Detection
Tasks
Classification, Classificatin+Localization, Object Detection, Instance Segmentation.
Localization, I:Image O:Box in the image(x,y,w,h) E:Intersection over Union.(IoU)
Claissification+Localization
Output: Single Label and Bounding Box
Idea one: Two task head
1.Train a CNN
2.Attach new fully-connected “regression head” to the network(FC)
2.1 Classification Head
2.2 Regression Head
3.Train the regression head only with SGD and L2 loss
4.At test time use both heads
Idea two: Sliding Window
Input: Bounding Box
Iteratively refine the BB into a optimal size and place.
Objects Detection
Output: all the exist labels and BBs
Problem: Need to test many positions and scales, use computationally demanding classifier
Solution: Only look at a tiny subset of possible positions
Region Proposal:Bottom-up segmentation.
RCNN: 1. Train a classification model on ImageNet
- Fine-tune model for detection(Throw finaly FC rathter than 20 Objects and one background, that is 4096*21 for the last layer.)
- Extract Features: Extract region proposals for all images, save the pool5 features to disk.
- Train one binary SVM per class to claissify region features.
- Bbox regression: Train a linear model to fine-grain the bbox
Fast-RCNN: Build an end-to-end framework, much faster than RCNN.
Summary
Lec9 Understanding and Visualizing
Visualizing the weights, t-SNE visualization
Deconv Approaches:
1.Feed image into net
2.pick a layer, set gradients of the score vector to [0 0 1 .. 0], then bp to image
3.Do a small “Image Update”
4.Forward the Image
- Go to step 2
Deconv
Learn to visualize the weights, also deconv to reconstruct an larger size output.
Deconv: reverse the convolution filter
DePool: record the position and set other be zero.
DeReLU: The same as the ReLU.
Neural Style
- extract content targets
- extract style targets
- Optimize over image
BackPropping is powerful
- Understanding
- Segmenting Objects in the Image
- Inverting codes and introducing privacy concerns
- Fun(NeuralStyle/DeepDream)
- Confusion and chaos(Adversarial Examples)
